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Speakers: Assoc Prof Devanand Anantham, Dr Shizuko Takahashi, Assoc Prof Brian Earp
Assoc Prof Devanand Anantham's Topic: And we became more like friends: Reimagining doctor-patient relationships Abstract: |
Dr Shizuko Takahashi's Topic: Reevaluating ‘Seriousness’ in Genetic Conditions: Balancing Clinical Criteria and Lived Experiences in Japan How should PGT-M criteria be determined? We begin with a Japanese case that catalyzed debate under a policy permitting only 16 conditions until 2022 (now ~36), which the Japanese Society of Obstetrics and Gynecology still finds ethically difficult to adjudicate. Building on Kleiderman et al., criteria should pair clinical rigor with lived-experience evidence in a transparent, revisable, publicly accountable process. In our Japan public survey (n=455), acceptance tracked perceived “seriousness” along a spectrum—avoid disability → avoid disease → select for health → select for enhancement → select for disability—with preferences clustering at the preventive end: high support to avoid disability (>70%) and hereditary cancers (66%); mixed for ADHD (62%); lower for appearance-related traits (48%). Endorsement of enhancement was rare (7%) and declined after testimony. These gradients map onto clinical dimensions (burden, age of onset, treatability, stigma) and can inform weights in a multi-criteria matrix. Procedurally, fairness and patient-centred impacts require multidisciplinary panels—including clinicians, ethicists, disability advocates, and people with lived experience—and structured testimony. Our stakeholder workshop aligns: 71% would choose PGT-M for hereditary cancer; 89% favored introducing PGT-M information at diagnosis; testimonies shifted views from “societal good” to reproductive rights (p=0.005). We therefore recommend formal public-and-patient involvement in setting and revising PGT-M criteria. Learning Outcomes:
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Assoc Prof Brian Earp's Topic: Digital Psychological Twins in Healthcare Rapid advances in artificial intelligence are making it possible to create digital “psychological twins” of individual patients—AI models fine-tuned on a person’s own writings, speech, and behavioural data to approximate their distinctive values, preferences, and reasoning patterns. In healthcare, such systems could help clinicians anticipate what a patient would want in complex or uncertain circumstances, especially when the patient cannot speak for themselves. One proposed application is the Personalised Patient Preference Predictor (P4), which aims to model individual decision tendencies with greater nuance than population-level surrogates. This talk explores the technical promise and ethical implications of such AI “mind mirrors.” It considers questions of consent, authenticity, and moral responsibility, asking whether these systems extend or distort patient autonomy, and how they might be responsibly designed, validated, and governed within healthcare decision-making. Ultimately, digital twins could humanise medicine—or mechanise it—depending on how we proceed. |
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